6 citations · 9 across the 9 of their papers we have counts for
10 papers
Rethinking Training-Inference Mismatch in LLM Reinforcement Learning: Where It Arises and How to Correct It
Tianrun Yu, Kaixiang Zhao, Shangzhe Li +4
We study training-inference mismatch in reinforcement learning with verifiable rewards (RLVR) for large language models, where rollouts are sampled by an inference engine while gra…
Self-Correction Can Amplify Hallucinations: Fact-Level Repair with Graph-Based Evidence Routing in Multimodal Generation
Kaixiang Zhao, Tianrun Yu, Shawn Huang +3
We study fact-level repair for multimodal generation, where a fluent output may contain specific facts that are not supported by the input. Existing inference-time repair methods o…
LARK: Learnability-Grounded Trajectory Selection for Efficient Reasoning Distillation
Tianrun Yu, Kaixiang Zhao, Chih-Chun Chen +5
We study trajectory selection for reasoning distillation, where teacher-generated reasoning trajectories are selectively used as supervision for a student model. Existing methods r…
FRAME: Forensic Routing and Adaptive Multi-path Evidence Fusion for Image Manipulation Detection
Kaixiang Zhao, Tianrun Yu, Aoxu Zhang +3
The proliferation of sophisticated image editing tools and generative artificial intelligence models has made verifying the authenticity of digital images increasingly challenging,…
Provable and Practical In-Context Policy Optimization for Self-Improvement
Tianrun Yu, Yuxiao Yang, Zhaoyang Wang +6
We study test-time scaling, where a model improves its answer through multi-round self-reflection at inference. We introduce In-Context Policy Optimization (ICPO), in which an agen…
Personalized Product Assortment with Real-time 3D Perception and Bayesian Payoff Estimation
Porter Jenkins, Michael Selander, J. Stockton Jenkins +2
Product assortment selection is a critical challenge facing physical retailers. Effectively aligning inventory with the preferences of shoppers can increase sales and decrease out-…
Fully Heteroscedastic Count Regression with Deep Double Poisson Networks
Spencer Young, Porter Jenkins, Longchao Da +2
Neural networks capable of accurate, input-conditional uncertainty representation are essential for real-world AI systems. Deep ensembles of Gaussian networks have proven highly ef…
Assessing the Probabilistic Fit of Neural Regressors via Conditional Congruence
Spencer Young, Riley Sinema, Cole Edgren +3
While significant progress has been made in specifying neural networks capable of representing uncertainty, deep networks still often suffer from overconfidence and misaligned pred…
Probabilistic Offline Policy Ranking with Approximate Bayesian Computation
Longchao Da, Porter Jenkins, Trevor Schwantes +2
In practice, it is essential to compare and rank candidate policies offline before real-world deployment for safety and reliability. Prior work seeks to solve this offline policy r…
A Probabilistic Simulator of Spatial Demand for Product Allocation
Porter Jenkins, Hua Wei, J. Stockton Jenkins +1
Connecting consumers with relevant products is a very important problem in both online and offline commerce. In physical retail, product placement is an effective way to connect co…